Evidence map›Paper›PMID 38875591›Full record

ArticleJMIR AI2024

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study.

Wilson Wb Goh, Kendrick Ya Chia, Max Fk Cheung, Kalya M Kee, May O Lwin, Peter J Schulz, Minhu Chen, Kaichun Wu, Simon Sm Ng, Rashid Lui and 5 more

Abstract read
In one paragraph

Article in JMIR AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Wilson Wb GohLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0003-3863-7501
Kendrick Ya ChiaLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0009-0002-4005-7899
Max Fk CheungLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0003-1968-5647
Kalya M KeeLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0001-9454-1534
May O LwinWee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore.ORCID https://orcid.org/0000-0003-1832-8242
Peter J SchulzLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0003-4281-489X
Minhu ChenThe First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0003-2421-9966
Kaichun WuXijing Hospital, Fourth Military Medical University, Xi'an, China.ORCID https://orcid.org/0000-0002-0699-9666
Simon Sm NgDepartment of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).ORCID https://orcid.org/0000-0002-5389-9297
Rashid LuiPrince of Wales Hospital, Hospital Authority, Hong Kong, China (Hong Kong).ORCID https://orcid.org/0000-0003-1277-9595
Tiing Leong AngDepartment of Gastroenterology and Hepatology, Changi General Hospital, SingHealth, Singapore, Singapore.ORCID https://orcid.org/0000-0001-9993-8549
Khay Guan YeohDepartment of Gastroenterology and Hepatology, National University Hospital, National University Health System, Singapore, Singapore.ORCID https://orcid.org/0000-0002-7802-4606
Han-Mo ChiuDepartment of Internal Medicine, National Taiwan University Hospital, Taiwan, China.ORCID https://orcid.org/0000-0003-2786-8056
Deng-Chyang WuKaohsiung Medical University, Taiwan, China.ORCID https://orcid.org/0000-0003-3742-0634
Joseph Jy SungLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore, Singapore.ORCID https://orcid.org/0000-0003-3125-5199

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively.

objectiveThis study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy.

methodsWe conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi).

resultsIn total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings.

conclusionsGastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

Indexed as

acceptanceadoptionartificial intelligencecolonoscopycolorectaldelivery of health caredetectdetectiongastroenterologistgastroenterologistsgastroenterologyinternal medicinepolyppolypsquestionnairequestionnairessurgeonsurgeonssurgerysurgicalsurveysurveystrust

Identifiers

PMID38875591
PMCPMC11041476

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.